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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Yihui Li, David Earl Hostallero, Amin Emad
Bioinformatics (2023) Vol. 39, Iss. 6
Open Access | Times Cited: 11

Showing 11 citing articles:

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Alexander Partin, Thomas Brettin, Yitan Zhu, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 72
Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress
Xiaoyu Zhao, Akshat Singhal, Sungjoon Park, et al.
Cancer Discovery (2024) Vol. 14, Iss. 3, pp. 508-523
Open Access | Times Cited: 13
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction
Lea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 6
The Hallmarks of Predictive Oncology
Akshat Singhal, Xiaoyu Zhao, Patrick D. Wall, et al.
Cancer Discovery (2025) Vol. 15, Iss. 2, pp. 271-285
Closed Access
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 3
Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives
Ezequiel Anokian, Judith Bernett, Adrian Freeman, et al.
(2024)
Open Access | Times Cited: 2
Machine Learning and Artificial Intelligence in Drug Repurposing—Challenges and Perspectives
Ezequiel Anokian, Judith Bernett, Adrian Freeman, et al.
Drug repurposing (2024) Vol. 1, Iss. 1
Closed Access | Times Cited: 2
Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction
Yurui Chen, Louxin Zhang
Bioinformatics (2024) Vol. 40, Iss. 4
Open Access | Times Cited: 1
Machine Learning and Artificial Intelligence in drug repurposing – challenges and perspectives
Ezequiel Anokian, Judith Bernett, Adrian Freeman, et al.
(2024)
Open Access
DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations
Iljung Jin, Songyeon Lee, Martin Schmuhalek, et al.
Briefings in Bioinformatics (2024) Vol. 26, Iss. 1
Open Access
Understanding the Sources of Performance in Deep Drug Response Models Reveals Insights and Improvements
Nikhil Branson, Pedro R. Cutillas, Conrad Bessant
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Optimizing drug synergy prediction through categorical embeddings in Deep Neural Networks
Manuel González Lastre, Pablo González de Prado Salas, Raúl Guantes
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
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